As exploited in [1]}, the feature map of each channel have the locality that a particular area in one feature map is activated.
Inspired by this, we visualize the feature map generated by VGG16 trained on ImageNet to explore the local information in the internal 13-th convolutional
layer.
As can be seen from Figure REF , the 52-th channel in the 13-th convolutional layer always generates feature map that highlights the head information while the 127-th channel always highlights the textual information.
Even though there is no explicitly labeled head or text, the class-discriminative channels automatically learns to extract partial information to make better decisions, which exactly meets human intuition when classifying an image. That is, head information extracted by the 52-th channel helps the model to identify animals, and textual information extracted by the 127-th channel contributes to classify categories with texts such as airliners and digital watches.
On the other hand, some local features may not be beneficial to identifying all categories. For example, the 127-th channel always chooses to deactivate most of the pixels when processing images with no textual semantics like cats and dogs (see the first and fourth columns).
